The World Report

I. Introduction:

Team clearly described the dataset and clearly described the motivation behind studying the data. Team provided scholarly citations or quantitative facts to describe the motivation.

II. Data Cleaning and Outlier Visualization:

Team clearly described their data cleaning and outlier removal process. Team presented insightful visualizations motivating to do further exploratory or confirmatory analysis.

#PART 1: Read csv, merge, clean and plot outliers.
library(readr)
library(readxl)
library(dplyr)
library(countrycode)
library(car)

source('Read_Clean.R')
cleaned <- Read_Clean()

III. Dimension Reduction Analysis:

Team applied dimension reduction analysis correctly and discussed the motivation behind that. Also, they provided interesting insights into the results.

Part A: MDS

• ALL VARIABLES INCLUDED -we can see Clusters -Asia: we can see is the most spread out and has the most outliers, spreads across Africa to Europe -Africa is the opposite of Europe -South America and North America are similar

#PART 2: MDS

image

# PART 3: PCA
library(pryr)
library(ggbiplot) #if the library is not present use the code below
#library(devtools)
#install_github("vqv/ggbiplot")
source('PCA.R')
(PrinCompPlot <- PCA(cleaned))

## [[1]]

## 
## [[2]]

FROM PCA excluded are: pop_total, murder_pp, armed_pp, urban_pop_tot, investment_per_of_GDP it is because they spoil correlation between variables and as such more PC would be needed to explain the relation.

PCA MEANING:

PC1: Developed countries, HIGH loaded in: phones, life exp., less corrup., internet income

PC2: sex ratio is high, suicide low

PC3: inequality is high

• ellipses to help visualize concentration of points. The size of ellipses is influenced by outlines.

• on the right we have develope countires: EU, North America where the most correlated are: less corruption, internet, life exp., phones, income.
• on the left have not as developed countries: Africa where Child mortality and children per woman and inequality is high. -Asia is the most diverse continent (especially in PC2). It also has the highest sex ration (means more mean per woman). Super low PC2 for sex ration (Quatar, UAE)

• Central America & South are in the middle on the plot

• on PC1 & PC3 explain better Africa. (it is because PC3 is mostly about inequality and development)

# PART 3: Hierarchical Clustering between Continents
library(ape)
source('cluster_continents.R')
Cl_continents <- cluster_continents(cleaned)

Include all variables

South, North and Europe are very similar. AND C America, Asia, Oceania and Africa are similar. Interesting is Africa is clustered with Oceania (with include Australia and NZ but also many small island which push Oceania into level of Africa)

# PART 4: K-means & Model Based Clustering between Countries
library(mclust)
library(maptools)
source('clusters_countries.R')
Cl_countries <- clusters_countries(cleaned)



• compare chi.square test -> dependency between groups and continents. Model based groups are more similar to continents.

• model based (group7) difficult name (result for this group) pop_total murder_pp armed_pp phones_p100 children_p_woman life_exp_yrs suicide_pp urban_pop_tot sex_ratio_p100 [1,] 239114394 0 0.011 146.317 2.07 78.053 0 118882678 148.681 corruption_CPI internet_%of_pop child_mort_p1000 income_per_person investments_per_ofGDP gini [1,] 53.677 75.725 10.791 50579.31 29.942 39.722

• Developed countries are split into 3 groups.

• poor countries are the same in both models

• we lost “crowded” group from k-means. It transfoms into group 7 which describe high Income, Sex Ratio, Population, phones

#PART 5: EFA
source('EFA.R')
EFA(cleaned)

## 
## Loadings:
##                       Factor1 Factor2 Factor3 Factor4
## pop_total                      0.995                 
## murder_pp                              0.825         
## armed_pp                                             
## phones_p100            0.615                         
## children_p_woman      -0.918                         
## life_exp_yrs           0.875                         
## suicide_pp                                           
## urban_pop_tot                  0.958                 
## sex_ratio_p100                                 0.538 
## corruption_CPI         0.538                         
## internet_%of_pop       0.847                         
## child_mort_p1000      -0.940                         
## income_per_person      0.575                   0.768 
## investments_per_ofGDP                                
## gini                                   0.625         
## 
##                Factor1 Factor2 Factor3 Factor4
## SS loadings      4.439   1.949   1.262   1.153
## Proportion Var   0.296   0.130   0.084   0.077
## Cumulative Var   0.296   0.426   0.510   0.587

## NULL

#PART 6: CFA
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